Many web pages are semantic diverse. That is, the whole content of a web page is not consistent to address one topic. However, current search engines are page-oriented (other than topic-oriented). But, most web users retrieve their target information by topics. Therefore, how to partition web pages by semantics is one of interesting research topics. In this paper, we firstly build a tree (called Semantic Tree, ST) to partition the web page into the content parts (called Semantic Part, SP) based on the web page tags. Then we analyze the characteristics of the words (or terms) appearing on the web page in order to build a term weighting formula. Based on these term weight values we employ the similarity formula to calculate the semantic similar degree between each two SPs. Finally, we consider the balance point of precision and recall as the reference value of the similarity-threshold. Through the work above we can find the content-related parts (or segmentations) of a web page. And we achieved a satisfied result.